CEO Power, CEO Compensation, and Firm Performance
Bibliographic record
Abstract
We investigate the impact of CEO power on the relation between CEO compensation and firm performance to find how CEO incentive compensation affects firm performance by reducing agency conflicts between managers and shareholders. We measure the CEO pay slice (CPS) for CEO power and the pay-performance sensitivity (PPS) for CEO incentive compensation. Employing standard control variables, we run multiple OLS regressions and show that PPS increases firm performance at the high level of CPS, but the impact of PPS decreases at the low level of CPS. To resolve the potential endogeneity concerns, we perform robustness checks by adopting instrumental variables in a two-stage least square (2SLS) estimation. We also consider the year-effect and find that our results remain the same as before. The finding implies that considering stand-alone associations of either PPS or CPS with firm performance—a common practice in the literature—will not be appropriate because there is an interaction effect between CEO power and incentive compensation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".